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Erschienen in: International Journal of Machine Learning and Cybernetics 1/2015

01.02.2015 | Original Article

An experimental study on stability and generalization of extreme learning machines

verfasst von: Aimin Fu, Chunru Dong, Laisheng Wang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2015

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Abstract

This paper gives an experimental study on the stability of an extreme learning machine (ELM) and its generalization capability. Focusing on the relationship between uncertainty of an ELM’s output on the training set and the ELM’s generalization capability, the experiments show some new results in the viewpoint of classical pattern recognition. The study provides some useful guidelines to choose a fraction of ELMs with better generalization from an ensemble for classification problems.

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Literatur
1.
Zurück zum Zitat Schmidt WF, Kraaijveld MA, Duin PW (1992) Feed-forward neural networks with random weights. Proceedings of 11th IAPR International Conference on Pattern Recognition Methodology and Systems, 2: 1–4 Schmidt WF, Kraaijveld MA, Duin PW (1992) Feed-forward neural networks with random weights. Proceedings of 11th IAPR International Conference on Pattern Recognition Methodology and Systems, 2: 1–4
2.
Zurück zum Zitat Igelnik B, Pao YH (1995) Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans Neural Netw 6(6):1320–1329CrossRef Igelnik B, Pao YH (1995) Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans Neural Netw 6(6):1320–1329CrossRef
3.
Zurück zum Zitat Li JY, Chow WS, Igelnik B, Pao YH (1997) Comments on “Stochastic choice of basis functions in adaptive function approximation and the functional-link net”. IEEE Trans Neural Netw 8(2):452–454 Li JY, Chow WS, Igelnik B, Pao YH (1997) Comments on “Stochastic choice of basis functions in adaptive function approximation and the functional-link net”. IEEE Trans Neural Netw 8(2):452–454
4.
Zurück zum Zitat Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355MATHMathSciNet Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355MATHMathSciNet
5.
Zurück zum Zitat Lowe D (1989) Adaptive radial basis function nonlinearities, and the problem of generalization. In: Proceedings of the 1st IEEE Conference on Artificial Neural Networks, London, UK, pp 171–175 Lowe D (1989) Adaptive radial basis function nonlinearities, and the problem of generalization. In: Proceedings of the 1st IEEE Conference on Artificial Neural Networks, London, UK, pp 171–175
6.
Zurück zum Zitat Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feed-forward neural networks. In: Proceedings of 2004 IEEE International Joint Conference on Neural Network, 2: 985–990 Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feed-forward neural networks. In: Proceedings of 2004 IEEE International Joint Conference on Neural Network, 2: 985–990
7.
Zurück zum Zitat Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRef Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRef
8.
Zurück zum Zitat Wang Y, Cao F, Yuan Y (2001) A study on effectiveness of extreme learning machine. Neurocomputing 74(16):2483–2490CrossRef Wang Y, Cao F, Yuan Y (2001) A study on effectiveness of extreme learning machine. Neurocomputing 74(16):2483–2490CrossRef
9.
Zurück zum Zitat Wang X, Chen A, Feng H (2001) Upper integral network with extreme learning mechanism. Neurocomputing 74(16):2520–2525CrossRef Wang X, Chen A, Feng H (2001) Upper integral network with extreme learning mechanism. Neurocomputing 74(16):2520–2525CrossRef
10.
Zurück zum Zitat Wang XZ, Shao QY, Qing M, Zhai JH (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102:3–9CrossRef Wang XZ, Shao QY, Qing M, Zhai JH (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102:3–9CrossRef
11.
Zurück zum Zitat Wang R, Kwong S, Wang XZ (2012) A study on random weights between input and hidden layers in extreme learning machine. Soft Comput 16(9):1465–1475CrossRef Wang R, Kwong S, Wang XZ (2012) A study on random weights between input and hidden layers in extreme learning machine. Soft Comput 16(9):1465–1475CrossRef
12.
Zurück zum Zitat Wu J, Wang ST, Chung FL (2011) Positive and negative fuzzy rule system, extreme learning machine and image classification. Int J Mach Learn Cybernet 2(4):261–271CrossRef Wu J, Wang ST, Chung FL (2011) Positive and negative fuzzy rule system, extreme learning machine and image classification. Int J Mach Learn Cybernet 2(4):261–271CrossRef
13.
Zurück zum Zitat Chacko BP, Krishnan VRV, Raju G, Anto PB (2012) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cybernet 3(2):149–161CrossRef Chacko BP, Krishnan VRV, Raju G, Anto PB (2012) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cybernet 3(2):149–161CrossRef
14.
Zurück zum Zitat Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybernet 2(2):107–122CrossRef Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybernet 2(2):107–122CrossRef
15.
Zurück zum Zitat Zhai JH, Xu HY, Wang XZ (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16(9):1493–1502CrossRef Zhai JH, Xu HY, Wang XZ (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16(9):1493–1502CrossRef
17.
Zurück zum Zitat Zhai J, Xu H, Li Y (2013) Fusion of extreme learning machine with fuzzy integral. Int J Uncertain Fuzziness Knowl Based Systems 21(2):23–34CrossRefMathSciNet Zhai J, Xu H, Li Y (2013) Fusion of extreme learning machine with fuzzy integral. Int J Uncertain Fuzziness Knowl Based Systems 21(2):23–34CrossRefMathSciNet
19.
Zurück zum Zitat Courrieu P (2005) Fast computation of moore-penrose inverse matrices. Neural Inf Process Lett Rev 8(2):25–29 Courrieu P (2005) Fast computation of moore-penrose inverse matrices. Neural Inf Process Lett Rev 8(2):25–29
20.
Zurück zum Zitat Deluca A, Termini S (1972) A definition of non-probabilistic entropy in the setting of fuzzy sets theory. Inf Control 20:301–312CrossRefMathSciNet Deluca A, Termini S (1972) A definition of non-probabilistic entropy in the setting of fuzzy sets theory. Inf Control 20:301–312CrossRefMathSciNet
22.
Zurück zum Zitat Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12:993–1001CrossRef Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12:993–1001CrossRef
23.
24.
Zurück zum Zitat Wang XZ, Dong CR (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567CrossRef Wang XZ, Dong CR (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567CrossRef
Metadaten
Titel
An experimental study on stability and generalization of extreme learning machines
verfasst von
Aimin Fu
Chunru Dong
Laisheng Wang
Publikationsdatum
01.02.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0238-0

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